Refactor random uniform ops and complete gamma and poisson

mindspore/ops/_op_impl/aicpu/gamma.py

@@ -23,6 +23,7 @@ gamma_op_info = AiCPURegOp("Gamma") \
     .input(2, "beta", "required") \
     .output(0, "output", "required") \
     .attr("seed", "int") \
+    .attr("seed2", "int") \
     .dtype_format(DataType.I32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
     .dtype_format(DataType.I32_NCHW, DataType.F32_NCHW, DataType.F32_NCHW, DataType.F32_NCHW) \
     .get_op_info()

mindspore/ops/_op_impl/aicpu/poisson.py

@@ -22,6 +22,7 @@ poisson_op_info = AiCPURegOp("Poisson") \
     .input(1, "mean", "required") \
     .output(0, "output", "required") \
     .attr("seed", "int") \
+    .attr("seed2", "int") \
     .dtype_format(DataType.I32_Default, DataType.F32_Default, DataType.I32_Default) \
     .dtype_format(DataType.I32_NCHW, DataType.F32_NCHW, DataType.I32_NCHW) \
     .get_op_info()

mindspore/ops/_op_impl/aicpu/uniform_int.py

@@ -23,6 +23,7 @@ uniform_int_op_info = AiCPURegOp("UniformInt") \
     .input(2, "b", "required") \
     .output(0, "output", "required") \
     .attr("seed", "int") \
+    .attr("seed2", "int") \
     .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
     .dtype_format(DataType.I32_NCHW, DataType.I32_NCHW, DataType.I32_NCHW, DataType.I32_NCHW) \
     .get_op_info()

mindspore/ops/_op_impl/aicpu/uniform_real.py

@@ -19,12 +19,11 @@ from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataTyp
 uniform_real_op_info = AiCPURegOp("UniformReal") \
     .fusion_type("OPAQUE") \
     .input(0, "shape", "required") \
-    .input(1, "a", "required") \
-    .input(2, "b", "required") \
     .output(0, "output", "required") \
     .attr("seed", "int") \
-    .dtype_format(DataType.I32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
-    .dtype_format(DataType.I32_NCHW, DataType.F32_NCHW, DataType.F32_NCHW, DataType.F32_NCHW) \
+    .attr("seed2", "int") \
+    .dtype_format(DataType.I32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_NCHW, DataType.F32_NCHW) \
     .get_op_info()
 
 @op_info_register(uniform_real_op_info)

mindspore/ops/composite/__init__.py

@@ -27,7 +27,7 @@ from .clip_ops import clip_by_value
 from .multitype_ops.add_impl import hyper_add
 from .multitype_ops.ones_like_impl import ones_like
 from .multitype_ops.zeros_like_impl import zeros_like
-from .random_ops import normal
+from .random_ops import set_seed, normal, uniform
 
 
 __all__ = [
@@ -48,5 +48,7 @@ __all__ = [
     'zeros_like',
     'ones_like',
     'zip_operation',
+    'set_seed',
+    'uniform',
     'normal',
     'clip_by_value',]

mindspore/ops/composite/random_ops.py

@@ -13,10 +13,13 @@
 # limitations under the License.
 # ============================================================================
 
-"""Operations for random number generatos."""
+"""Operations for random number generators."""
 
-from mindspore.ops.primitive import constexpr
 from .. import operations as P
+from .. import functional as F
+from ..primitive import constexpr
+from .multitype_ops import _constexpr_utils as const_utils
+from ...common import dtype as mstype
 
 # set graph-level RNG seed
 _GRAPH_SEED = 0
@@ -31,17 +34,17 @@ def get_seed():
     return _GRAPH_SEED
 
 
-def normal(shape, mean, stddev, seed):
+def normal(shape, mean, stddev, seed=0):
     """
     Generates random numbers according to the Normal (or Gaussian) random number distribution.
     It is defined as:
 
     Args:
-        - **shape** (tuple) - The shape of random tensor to be generated.
-        - **mean** (Tensor) - The mean μ distribution parameter, which specifies the location of the peak.
+        shape (tuple): The shape of random tensor to be generated.
+        mean (Tensor): The mean μ distribution parameter, which specifies the location of the peak.
           With float32 data type.
-        - **stddev** (Tensor) - The deviation σ distribution parameter. With float32 data type.
-        - **seed** (int): Seed is used as entropy source for Random number engines generating pseudo-random numbers.
+        stddev (Tensor): The deviation σ distribution parameter. With float32 data type.
+        seed (int): Seed is used as entropy source for Random number engines generating pseudo-random numbers.
           Default: 0.
 
     Returns:
@@ -52,12 +55,58 @@ def normal(shape, mean, stddev, seed):
         >>> shape = (4, 16)
         >>> mean = Tensor(1.0, mstype.float32)
         >>> stddev = Tensor(1.0, mstype.float32)
+        >>> C.set_seed(10)
         >>> output = C.normal(shape, mean, stddev, seed=5)
     """
-    set_seed(10)
+    mean_dtype = F.dtype(mean)
+    stddev_dtype = F.dtype(stddev)
+    const_utils.check_tensors_dtype_same(mean_dtype, mstype.float32, "normal")
+    const_utils.check_tensors_dtype_same(stddev_dtype, mstype.float32, "normal")
     seed1 = get_seed()
     seed2 = seed
     stdnormal = P.StandardNormal(seed1, seed2)
     rnd = stdnormal(shape)
     value = rnd * stddev + mean
     return value
+
+def uniform(shape, a, b, seed=0, dtype=mstype.float32):
+    """
+    Generates random numbers according to the Uniform (or Gaussian) random number distribution.
+    It is defined as:
+
+    Args:
+        shape (tuple): The shape of random tensor to be generated.
+        a (Tensor): The a distribution parameter.
+          It defines the minimum possibly generated value. With int32 or float32 data type.
+          If dtype is int32, only one number is allowed.
+        b (Tensor): The b distribution parameter.
+          It defines the maximum possibly generated value. With int32 or float32 data type.
+          If dtype is int32, only one number is allowed.
+        seed (int): Seed is used as entropy source for Random number engines generating pseudo-random numbers.
+          Default: 0.
+
+    Returns:
+        Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of a and b.
+        The dtype is float32.
+
+    Examples:
+        >>> shape = (4, 16)
+        >>> a = Tensor(1.0, mstype.float32)
+        >>> b = Tensor(1.0, mstype.float32)
+        >>> C.set_seed(10)
+        >>> output = C.uniform(shape, a, b, seed=5)
+    """
+    a_dtype = F.dtype(a)
+    b_dtype = F.dtype(b)
+    const_utils.check_tensors_dtype_same(a_dtype, dtype, "uniform")
+    const_utils.check_tensors_dtype_same(b_dtype, dtype, "uniform")
+    seed1 = get_seed()
+    seed2 = seed
+    if const_utils.is_same_type(dtype, mstype.int32):
+        rnd = P.UniformInt(seed1, seed2)
+        value = rnd(shape, a, b)
+    else:
+        uniform_real = P.UniformReal(seed1, seed2)
+        rnd = uniform_real(shape)
+        value = rnd * (b - a) + a
+    return value

mindspore/ops/operations/random_ops.py

@@ -34,8 +34,7 @@ class StandardNormal(PrimitiveWithInfer):
         - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
 
     Outputs:
-        Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of mean and stddev.
-        The dtype is float32.
+        Tensor. The shape that the input 'shape' denotes. The dtype is float32.
 
     Examples:
         >>> shape = (4, 16)
@@ -126,8 +125,8 @@ class Gamma(PrimitiveWithInfer):
         \text{P}(x|α,β) = \frac{\exp(-x/β)}{{β^α}\cdot{\Gamma(α)}}\cdot{x^{α-1}},
 
     Args:
-        seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers.
-          Default: 0.
+        seed (int): Random seed. Default: 0.
+        seed2 (int): Random seed2. Default: 0.
 
     Inputs:
         - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
@@ -149,10 +148,11 @@ class Gamma(PrimitiveWithInfer):
     """
 
     @prim_attr_register
-    def __init__(self, seed=0):
+    def __init__(self, seed=0, seed2=0):
         """Init Gamma"""
         self.init_prim_io_names(inputs=['shape', 'alpha', 'beta'], outputs=['output'])
         validator.check_value_type('seed', seed, [int], self.name)
+        validator.check_value_type('seed2', seed2, [int], self.name)
 
     def __infer__(self, shape, alpha, beta):
         shape_v = shape["value"]
@@ -180,8 +180,8 @@ class Poisson(PrimitiveWithInfer):
         \text{P}(i|μ) = \frac{\exp(-μ)μ^{i}}{i!},
 
     Args:
-        seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers.
-          Default: 0.
+        seed (int): Random seed. Default: 0.
+        seed2 (int): Random seed2. Default: 0.
 
     Inputs:
         - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
@@ -200,10 +200,11 @@ class Poisson(PrimitiveWithInfer):
     """
 
     @prim_attr_register
-    def __init__(self, seed=0):
+    def __init__(self, seed=0, seed2=0):
         """Init Poisson"""
         self.init_prim_io_names(inputs=['shape', 'mean'], outputs=['output'])
         validator.check_value_type('seed', seed, [int], self.name)
+        validator.check_value_type('seed2', seed2, [int], self.name)
 
     def __infer__(self, shape, mean):
         shape_v = shape["value"]
@@ -223,7 +224,7 @@ class Poisson(PrimitiveWithInfer):
 
 class UniformInt(PrimitiveWithInfer):
     r"""
-    Produces random integer values i, uniformly distributed on the closed interval [a, b], that is,
+    Produces random integer values i, uniformly distributed on the closed interval [a, b), that is,
     distributed according to the discrete probability function:
 
     .. math::
@@ -233,19 +234,18 @@ class UniformInt(PrimitiveWithInfer):
         The number in tensor a should be strictly less than b at any position after broadcasting.
 
     Args:
-        seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers.
-          Default: 0.
+        seed (int): Random seed. Default: 0.
+        seed2 (int): Random seed2. Default: 0.
 
     Inputs:
         - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
         - **a** (Tensor) - The a distribution parameter.
-          It defines the minimum possibly generated value. With int32 data type.
+          It defines the minimum possibly generated value. With int32 data type. Only one number is supported.
         - **b** (Tensor) - The b distribution parameter.
-          It defines the maximum possibly generated value. With int32 data type.
+          It defines the maximum possibly generated value. With int32 data type. Only one number is supported.
 
     Outputs:
-        Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of a and b.
-        The dtype is int32.
+        Tensor. The shape that the input 'shape' denotes. The dtype is int32.
 
     Examples:
         >>> shape = (4, 16)
@@ -256,10 +256,11 @@ class UniformInt(PrimitiveWithInfer):
     """
 
     @prim_attr_register
-    def __init__(self, seed=0):
+    def __init__(self, seed=0, seed2=0):
         """Init UniformInt"""
         self.init_prim_io_names(inputs=['shape', 'a', 'b'], outputs=['output'])
         validator.check_value_type('seed', seed, [int], self.name)
+        validator.check_value_type('seed2', seed2, [int], self.name)
 
     def __infer__(self, shape, a, b):
         shape_v = shape["value"]
@@ -270,10 +271,12 @@ class UniformInt(PrimitiveWithInfer):
             validator.check_integer("shape[%d]" % i, shape_i, 0, Rel.GT, self.name)
         validator.check_tensor_type_same({"a": a["dtype"]}, [mstype.int32], self.name)
         validator.check_tensor_type_same({"b": b["dtype"]}, [mstype.int32], self.name)
-        broadcast_shape = get_broadcast_shape(a['shape'], b['shape'], self.name)
-        broadcast_shape = get_broadcast_shape(broadcast_shape, shape_v, self.name)
+        a_shape = a['shape']
+        b_shape = b['shape']
+        validator.check("dim of a", len(a_shape), '0(scalar)', 0, Rel.EQ, self.name)
+        validator.check("dim of b", len(b_shape), '0(scalar)', 0, Rel.EQ, self.name)
         out = {
-            'shape': broadcast_shape,
+            'shape': shape_v,
             'dtype': mstype.int32,
             'value': None}
         return out
@@ -281,54 +284,40 @@ class UniformInt(PrimitiveWithInfer):
 
 class UniformReal(PrimitiveWithInfer):
     r"""
-    Produces random floating-point values i, uniformly distributed on the interval [min(a, b), max(a, b)), that is,\
-    distributed according to the probability density function:
-
-    .. math::
-        \text{P}(i|a,b) = \frac{1}{b-a},
+    Produces random floating-point values i, uniformly distributed on the interval [0, 1).
 
     Args:
-        seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers.
-          Default: 0.
+        seed (int): Random seed. Default: 0.
+        seed2 (int): Random seed2. Default: 0.
 
     Inputs:
         - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
-        - **a** (Tensor) - The a distribution parameter.
-          It defines the minimum possibly generated value. With float32 data type.
-        - **b** (Tensor) - The b distribution parameter.
-          It defines the maximum possibly generated value. With float32 data type.
 
     Outputs:
-        Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of a and b.
-        The dtype is float32.
+        Tensor. The shape that the input 'shape' denotes. The dtype is float32.
 
     Examples:
         >>> shape = (4, 16)
-        >>> a = Tensor(1.0, mstype.float32)
-        >>> b = Tensor(5.0, mstype.float32)
-        >>> uniform_real = P.UniformReal(seed=10)
-        >>> output = uniform_real(shape, a, b)
+        >>> uniformreal = P.UniformReal(seed=2)
+        >>> output = uniformreal(shape)
     """
 
     @prim_attr_register
-    def __init__(self, seed=0):
+    def __init__(self, seed=0, seed2=0):
         """Init UniformReal"""
-        self.init_prim_io_names(inputs=['shape', 'a', 'b'], outputs=['output'])
+        self.init_prim_io_names(inputs=['shape'], outputs=['output'])
         validator.check_value_type('seed', seed, [int], self.name)
+        validator.check_value_type('seed2', seed2, [int], self.name)
 
-    def __infer__(self, shape, a, b):
+    def __infer__(self, shape):
         shape_v = shape["value"]
         if shape_v is None:
             raise ValueError(f"For {self.name}, shape must be const.")
         validator.check_value_type("shape", shape_v, [tuple], self.name)
         for i, shape_i in enumerate(shape_v):
             validator.check_integer("shape[%d]" % i, shape_i, 0, Rel.GT, self.name)
-        validator.check_tensor_type_same({"a": a["dtype"]}, [mstype.float32], self.name)
-        validator.check_tensor_type_same({"b": b["dtype"]}, [mstype.float32], self.name)
-        broadcast_shape = get_broadcast_shape(a['shape'], b['shape'], self.name)
-        broadcast_shape = get_broadcast_shape(broadcast_shape, shape_v, self.name)
         out = {
-            'shape': broadcast_shape,
+            'shape': shape_v,
             'dtype': mstype.float32,
             'value': None}
         return out

tests/st/ops/ascend/test_aicpu_ops/test_gamma.py

@@ -24,9 +24,9 @@ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 
 class Net(nn.Cell):
-    def __init__(self, shape, seed=0):
+    def __init__(self, shape, seed=0, seed2=0):
         super(Net, self).__init__()
-        self.gamma = P.Gamma(seed=seed)
+        self.gamma = P.Gamma(seed=seed, seed2=seed2)
         self.shape = shape
 
     def construct(self, alpha, beta):
@@ -38,10 +38,9 @@ def test_net_1D():
     shape = (3, 2, 4)
     alpha = 1.0
     beta = 1.0
-    net = Net(shape, seed)
+    net = Net(shape=shape, seed=seed)
     talpha, tbeta = Tensor(alpha, mstype.float32), Tensor(beta, mstype.float32)
     output = net(talpha, tbeta)
-    print(output.asnumpy())
     assert output.shape == (3, 2, 4)
 
 
@@ -50,9 +49,8 @@ def test_net_ND():
     shape = (3, 1, 2)
     alpha = np.array([[[1], [2]], [[3], [4]], [[5], [6]]]).astype(np.float32)
     beta = np.array([1.0]).astype(np.float32)
-    net = Net(shape, seed)
+    net = Net(shape=shape, seed=seed)
     talpha, tbeta = Tensor(alpha), Tensor(beta)
     output = net(talpha, tbeta)
-    print(output.asnumpy())
     assert output.shape == (3, 2, 2)
 

tests/st/ops/ascend/test_aicpu_ops/test_normal.py

@@ -32,6 +32,7 @@ class Net(nn.Cell):
         self.seed = seed
 
     def construct(self, mean, stddev):
+        C.set_seed(20)
         return C.normal(self.shape, mean, stddev, self.seed)
 
 
@@ -43,7 +44,6 @@ def test_net_1D():
     net = Net(shape, seed)
     tmean, tstddev = Tensor(mean, mstype.float32), Tensor(stddev, mstype.float32)
     output = net(tmean, tstddev)
-    print(output.asnumpy())
     assert output.shape == (3, 2, 4)
 
 
@@ -55,5 +55,4 @@ def test_net_ND():
     net = Net(shape, seed)
     tmean, tstddev = Tensor(mean, mstype.float32), Tensor(stddev, mstype.float32)
     output = net(tmean, tstddev)
-    print(output.asnumpy())
     assert output.shape == (3, 2, 2)

tests/st/ops/ascend/test_aicpu_ops/test_poisson.py

@@ -24,7 +24,7 @@ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 
 class Net(nn.Cell):
-    def __init__(self, shape):
+    def __init__(self, shape, seed=0, seed2=0):
         super(Net, self).__init__()
         self.poisson = P.Poisson()
         self.shape = shape
@@ -36,18 +36,16 @@ class Net(nn.Cell):
 def test_net_1():
     shape = (2, 16)
     mean = np.array([5.0]).astype(np.float32)
-    net = Net(shape)
+    net = Net(shape=shape)
     tmean = Tensor(mean)
     output = net(tmean)
-    print(output.asnumpy())
     assert output.shape == (2, 16)
 
 
 def test_net_2():
     shape = (4, 1)
     mean = np.array([5.0, 10.0]).astype(np.float32)
-    net = Net(shape)
+    net = Net(shape=shape)
     tmean = Tensor(mean)
     output = net(tmean)
-    print(output.asnumpy())
     assert output.shape == (4, 2)

tests/st/ops/ascend/test_aicpu_ops/test_standard_normal.py

@@ -34,7 +34,7 @@ class Net(nn.Cell):
         self.stdnormal = P.StandardNormal(seed, seed2)
 
     def construct(self):
-        return self.stdnormal(self.shape, self.seed, self.seed2)
+        return self.stdnormal(self.shape)
 
 
 def test_net():

tests/st/ops/ascend/test_aicpu_ops/test_uniform.py

@@ -0,0 +1,57 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.common import dtype as mstype
+from mindspore.ops import composite as C
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
+class Net(nn.Cell):
+    def __init__(self, shape, seed=0):
+        super(Net, self).__init__()
+        self.shape = shape
+        self.seed = seed
+
+    def construct(self, a, b):
+        C.set_seed(20)
+        return C.uniform(self.shape, a, b, self.seed)
+
+
+def test_net_1D():
+    seed = 10
+    shape = (3, 2, 4)
+    a = 1.0
+    b = 6.0
+    net = Net(shape, seed)
+    ta, tb = Tensor(a, mstype.float32), Tensor(b, mstype.float32)
+    output = net(ta, tb)
+    assert output.shape == (3, 2, 4)
+
+
+def test_net_ND():
+    seed = 10
+    shape = (3, 1, 2)
+    a = np.array([[[1], [2]], [[3], [4]], [[5], [6]]]).astype(np.float32)
+    b = np.array([1.0]).astype(np.float32)
+    net = Net(shape, seed)
+    ta, tb = Tensor(a, mstype.float32), Tensor(b, mstype.float32)
+    output = net(ta, tb)
+    assert output.shape == (3, 2, 2)

tests/st/ops/ascend/test_aicpu_ops/test_uniform_int.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-import numpy as np
 
 import mindspore.context as context
 import mindspore.nn as nn
@@ -24,7 +23,7 @@ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 
 class Net(nn.Cell):
-    def __init__(self, shape, seed=0):
+    def __init__(self, shape, seed=0, seed2=0):
         super(Net, self).__init__()
         self.uniformint = P.UniformInt(seed=seed)
         self.shape = shape
@@ -38,20 +37,7 @@ def test_net_1D():
     shape = (3, 2, 4)
     a = 1
     b = 5
-    net = Net(shape, seed)
+    net = Net(shape, seed=seed)
     ta, tb = Tensor(a, mstype.int32), Tensor(b, mstype.int32)
     output = net(ta, tb)
-    print(output.asnumpy())
     assert output.shape == (3, 2, 4)
-
-
-def test_net_ND():
-    seed = 10
-    shape = (3, 2, 1)
-    a = np.array([[[1, 2]], [[3, 4]], [[5, 6]]]).astype(np.int32)
-    b = np.array([10]).astype(np.int32)
-    net = Net(shape, seed)
-    ta, tb = Tensor(a), Tensor(b)
-    output = net(ta, tb)
-    print(output.asnumpy())
-    assert output.shape == (3, 2, 2)

tests/st/ops/ascend/test_aicpu_ops/test_uniform_real.py

@@ -12,46 +12,27 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-import numpy as np
 
 import mindspore.context as context
 import mindspore.nn as nn
-from mindspore import Tensor
 from mindspore.ops import operations as P
-from mindspore.common import dtype as mstype
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 
 class Net(nn.Cell):
-    def __init__(self, shape, seed=0):
+    def __init__(self, shape, seed=0, seed2=0):
         super(Net, self).__init__()
         self.uniformreal = P.UniformReal(seed=seed)
         self.shape = shape
 
-    def construct(self, a, b):
-        return self.uniformreal(self.shape, a, b)
+    def construct(self):
+        return self.uniformreal(self.shape)
 
 
-def test_net_1D():
+def test_net():
     seed = 10
     shape = (3, 2, 4)
-    a = 1.0
-    b = 5.0
-    net = Net(shape, seed)
-    ta, tb = Tensor(a, mstype.float32), Tensor(b, mstype.float32)
-    output = net(ta, tb)
-    print(output.asnumpy())
+    net = Net(shape, seed=seed)
+    output = net()
     assert output.shape == (3, 2, 4)
-
-
-def test_net_ND():
-    seed = 10
-    shape = (3, 2, 1)
-    a = np.array([[[1, 2]], [[3, 4]], [[5, 6]]]).astype(np.float32)
-    b = np.array([10]).astype(np.float32)
-    net = Net(shape, seed)
-    ta, tb = Tensor(a), Tensor(b)
-    output = net(ta, tb)
-    print(output.asnumpy())
-    assert output.shape == (3, 2, 2)

tests/ut/python/ops/test_ops.py

@@ -573,25 +573,14 @@ class PoissonNet(nn.Cell):
         return out
 
 
-class UniformIntNet(nn.Cell):
+class UniformNet(nn.Cell):
     def __init__(self, shape=None, seed=0):
-        super(UniformIntNet, self).__init__()
-        self.uniformint = P.UniformInt(seed=seed)
-        self.shape = shape
-
-    def construct(self, a, b):
-        out = self.uniformint(self.shape, a, b)
-        return out
-
-
-class UniformRealNet(nn.Cell):
-    def __init__(self, shape=None, seed=0):
-        super(UniformRealNet, self).__init__()
-        self.uniformreal = P.UniformReal(seed=seed)
+        super(UniformNet, self).__init__()
         self.shape = shape
+        self.seed = seed
 
     def construct(self, a, b):
-        out = self.uniformreal(self.shape, a, b)
+        out = C.uniform(self.shape, a, b, self.seed)
         return out
 
 
@@ -882,13 +871,9 @@ test_case_math_ops = [
         'block': PoissonNet((3, 2, 4), 0),
         'desc_inputs': [Tensor(2.0, mstype.float32)],
         'skip': ['backward']}),
-    ('UniformInt', {
-        'block': UniformIntNet((3, 2, 4), 0),
-        'desc_inputs': [Tensor(1, mstype.int32), Tensor(15, mstype.int32)],
-        'skip': ['backward']}),
-    ('UniformReal', {
-        'block': UniformRealNet((3, 2, 4), 0),
-        'desc_inputs': [Tensor(1.0, mstype.float32), Tensor(5.0, mstype.float32)],
+    ('Uniform', {
+        'block': UniformNet((3, 2, 4), 0),
+        'desc_inputs': [Tensor(0.0, mstype.float32), Tensor(1.0, mstype.float32)],
         'skip': ['backward']}),
     ('RandomChoiceWithMask', {
         'block': P.RandomChoiceWithMask(256),